A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation

DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assay still has limitations in the time consumed for manual scoring and possible variability between scorers. This study proposed a novel automated foci scoring method using a deep convolutional neural network based on a You-Only-Look-Once (YOLO) algorithm to quantify γ-H2AX foci in peripheral blood samples. FociRad, a two-stage deep learning approach, consisted of mononuclear cell (MNC) and γ-H2AX foci detections. Whole blood samples were irradiated with X-rays from a 6 MV linear accelerator at 1, 2, 4 or 6 Gy. Images were captured using confocal microscopy. Then, dose–response calibration curves were established and implemented with unseen dataset. The results of the FociRad model were comparable with manual scoring. MNC detection yielded 96.6% accuracy, 96.7% sensitivity and 96.5% specificity. γ-H2AX foci detection showed very good F1 scores (> 0.9). Implementation of calibration curve in the range of 0–4 Gy gave mean absolute difference of estimated doses less than 1 Gy compared to actual doses. In addition, the evaluation times of FociRad were very short (< 0.5 min per 100 images), while the time for manual scoring increased with the number of foci. In conclusion, FociRad was the first automated foci scoring method to use a YOLO algorithm with high detection performance and fast evaluation time, which opens the door for large-scale applications in radiation triage.


Scientific Reports
| (2022) 12:5527 | https://doi.org/10.1038/s41598-022-09180-2 www.nature.com/scientificreports/ Dataset acquisition. Blood collection and in vitro irradiation. Blood samples were collected from seven healthy donors (four male and three female volunteers, age 21-25 years) by venipuncture into 5 mL heparinized tubes after informed written consent. Donors were nonsmokers with good health (no obvious illness at time of donation) and no history of exposure to chemotherapy or radiotherapy within the last two years. Blood samples were aliquoted into 1.5 mL cryovials which were either irradiated with different amounts of X-rays (1, 2, 4 or 6 Gy) or not irradiated and kept as controls (0 Gy). A 3-dimensional radiotherapy plan consisted of 6 MV X-ray, dose rate of 600 MU/min, field size 10 cm 2 , source to surface distance of 95 cm; prescribed doses of 1, 2, 4 or 6 Gy at 5 cm depth were computed on a simulated water phantom using Acuros XB dose calculation algorithm in the Eclipse Treatment Planning System version 16.1 (Varian Medical Systems, Milpitas, CA). Tubes of sampled blood were set up under the treatment plan conditions, placed into the holder at a depth of 5 cm in the water phantom, and irradiated using a TrueBeam linear accelerator (Varian Medical Systems, Palo Alto, CA). In this study, the radiation dose at a depth of interest was determined using a Farmer chamber FC65-G (IBA Dosimetry, Germany), the standard clinical reference dosimeter for high-energy photons.
γ-H2AX assay and image acquisition. Irradiated blood samples were incubated at 37 °C for 1 h prior to transferring 500 µL of blood into 15 mL conical bottom tubes for fixation with 500 µL of 8% paraformaldehyde for 10 min. Fixed blood samples were diluted with 1 × PBS to make a final concentration of 1% paraformaldehyde before transporting on ice to a laboratory in the Faculty of Medical Technology, Mahidol University. Blood samples were centrifuged at 1000 rpm at 4 °C for 5 min and incubated in 0.1% (v/v) Triton X-100 diluted in phosphate-buffered saline (PBS) for 30 min. Cells were washed twice with PBS. After removal of red blood cells, WBCs were resuspended in 1 mL 50% methanol and placed on ice for 10 min before blocking in 1% bovine serum albumin (BSA) for 30 min. Cells were stained with Alexa Fluor 488 anti-H2A.X Phosphor (Ser139) (Bio-Legend, USA) (1:25) for 2 h, APC anti-human CD45 (BioLegend) (1:25) for 30 min and DAPI for 10 min. After incubation, cells were washed three times with PBS to remove excess antibodies, and stored in 1% paraformaldehyde overnight. Cells were carefully spread and mounted on glass slides and then kept at 4 °C prior to analysis.
Images were taken with a confocal laser scanning microscope (Zeiss LSM 800 Airyscan confocal laser scanning microscope, Carl Zeiss, Jena, Germany). Channel mode visualization was done using the 100X objective with oil immersion, resulting in images with a resolution of 1024 × 1024 pixels. Three color channels [DAPI (blue), Alexa Fluor 488 anti-H2A.X Phosphor (Ser139) (green), and APC anti-human CD45 (red)] captured confocal images of nuclei, γ-H2AX foci, and surface of leukocytes, respectively. MNCs were differentiated from polymorphonuclear cells by morphologically distinguishing double-positive DAPI and bright CD45 expression. Z-stack images (5 slices per cell) with a slice interval of 0.6 µm were captured and exported as CZI file format, then converted to TIFF file format using modular image acquisition, processing and analysis software for digital microscopy (ZEN 2.1 software, Carl Zeiss Microscopy GmbH). A total of 965 images with no overlapping of neighboring cells were captured to obtain 700 MNCs irradiated at 0, 1, 2, 4 or 6 Gy. Examples of confocal microscopic images of MNCs from different doses can be found in Supplementary Fig. S1 online.
FociRad: the concatenated deep learning model. The concatenated deep learning approach was proposed as a novel technique that consists of a two-stage deep learning model. The MNC detections in the first-stage model were implemented on captured images using the object detection-based YOLO v4 CNN 73 . Cropped rectangular image regions that were precisely fitted to each observed MNC served as input datasets for the second-stage CNN model. Therefore, there was no two cells visible in a single bounding box. However, with the rectangular bounding box of YOLO v4, there might be a part of the neighboring cells visible at the corner (mostly cell surface and a very small portion of the nucleus). In the second stage, this became a training dataset for the γ-H2AX foci detection model. Figure 1 illustrates the process flow for the proposed method's data preparation and use in model training. Fig. 1). Three color channels of the confocal images, which captured nucleus ( Fig. 2A), γ-H2AX foci (Fig. 2B), and the CD45 surface marker (Fig. 2C), were merged to generate RGB images (Fig. 2D) using MATLAB R2020a software (MathWorks, USA). During image capture in a typical γ-H2AX assay, there may be variation in intensity between experiments due to the staining and imaging processes. Thus, to eliminate such variation, the standard image processing method [i.e., histogram matching, 3 × 3 median filter, and Gaussian filters (standard deviation of 5)] was employed with all merged RGB images before they were used in the model training process.

Captured image preprocessing (label A in
Mononuclear cell detection model (first-stage model): dataset preparation and training. The purpose of this procedure was to create an object detection model for automatically detecting MNC regions on the captured images. To begin, the dataset was prepared through MNC annotation (label B in Fig. 1). A dataset of 690 images (560 cells) was chosen at random from the group of captured MNC images. MNC annotation was performed by a trained biologist. MNCs were differentiated from polymorphonuclear cells by morphologically distinguishing double-positive DAPI and bright CD45 expression in RGB images as mentioned previously. We used a rectangular bounding box to manually label each MNC on the RGB image and excluded cells near the surface as well as those with irregular shapes (incomplete cells). The bounding box's central point was situated in the center of each MNC. The captured images were manually labelled as "MNC" (Fig. 2E). Manually scored image sets were assembled and used to train the MNC detection model.
The labelled MNC images were imported into an in-house deep-learning model development platform (CiRA CORE, https:// web. faceb ook. com/ groups/ cira. core. comm/) to train the MNC detection-based CNN model (YOLO v4) (label C in Fig. 1 www.nature.com/scientificreports/ Figure 1. Schematic diagram of the proposed concatenated deep-learning approach (FociRad). Workflow starts from dataset acquisition process. The dataset was imported into the FociRad two-stage detection models, i.e. mononuclear cell (MNC) and foci detection models. D1-weighted for MNC detection (D1-W) was obtained from step A to D. Automated MNC detection and cropping process using D1-W was performed in step E and F. D2-weighted for foci detection (D2-W) was obtained from step G to I. Finally, the performance of FociRad model for counting number of foci using test dataset was evaluated in step J. γ-H2AX foci detection model (Second-stage model): dataset preparation and training. The dataset obtained from 635 images (500 cells) was used to develop the γ-H2AX foci detection model (label E in Fig. 1). The trained MNC detection model described as the first-stage model was employed with optimized D1-W parameters. It was used to prepare the cropped MNC images by operating with the captured images as input. The cropping operation was performed on the detected MNC regions (Fig. 3B) to obtain 2388 cropped MNC images (500 cells) (label F in Fig. 1). Model outputs corresponded to the data from the bounding boxes of MNCs. These bounding boxes were used to provide position and area for cropping the MNC images. Subsequently, extraction of green channel data (label G in Fig. 1) was performed to separate γ-H2AX foci areas (Fig. 3C) using MATLAB R2020b software (MathWorks). The γ-H2AX foci were manually labelled by a trained biologist (label H in Fig. 1). We used a rectangular bounding box to manually label each γ-H2AX foci on the cropped image. These cropped MNC images were manually labelled as "foci" (Fig. 3D). Manually scored image sets were assembled and used to train the model for γ-H2AX foci detection. The training dataset (1,904 cropped MNC images, 400 cells) was imported into our in-house, deep learning framework (CiRA CORE) to train the γ-H2AX foci detection-based CNN model (YOLO v4) (label I in Fig. 1). For training, Intel 1151 ®Core™ i5-9600 K CPU, NVIDIA GeForce RTX 2070 GPU and 250 GB RAM was used. The total computational time was 37 h, 1 min and 57 s. The parameters denoted as D2-weighted (D2-W) were obtained after the model training by choosing the best performance of the foci detection model. γ-H2AX foci detection model testing and performance evaluation. FociRad was employed to test 484 cropped images (100 cells) (label J in Fig. 1). Following that, the D2-W qualified weight was used for automatic γ-H2AX foci recognition. The experiments were carried out on a workstation with Intel 1151 ®Core™ i5-9600 K CPU, NVIDIA GeForce RTX 2070 GPU and 250 GB RAM. Figure 4 presents examples of automated foci detection using FociRad. The number of γ-H2AX foci in each cropped image was automatically counted and recorded in a Microsoft excel file. The mean number of foci per cell at each dose of irradiation was calculated. A video of the foci detection model being tested was posted on YouTube at https:// bit. ly/ 3zrBh cz.

Scientific Reports
To assess γ-H2AX foci detection performance, the indexes of precision and recall ratio were calculated as in Eqs. (1) and (2).   www.nature.com/scientificreports/ where TP is true positive, FP is false positive and FN is false negative. Notably, we did not use accuracy as a measure, as more than 90% of the training pixels were true negatives (TN), making accuracy and specificity insufficient metrics 60 . F1 score is a harmonic average of precision and recall rate, and can be defined as in Eq. (3).
In the calculation of F1 score, true positive (TP), false positive (FP) and false negative (FN) values were used which has been confirmed by manual scoring. Implementation in radiation dose estimation. We implemented the proposed FociRad for radiation dose estimation in an unseen dataset, which was collected from the blood sample of one healthy female donor (age, 21 years), then these dose estimates were compared with those obtained by manual scoring. The unseen dataset contained 80 MNCs irradiated with 0, 2, 4 or 6 Gy (160 captured images). All captured images were preprocessed before manual or automated scoring (see "Captured image preprocessing (label A in Fig. 1)" section).
For manual scoring, two independent observers performed the foci counting. Inter-rater reliability was determined to assess overall agreement between the two observers. The images were fully blinded before analysis and the two observers had no information about radiation dose exposure to the blood samples. Shrout-Fleiss 78 intraclass correlation coefficient (ICC) estimates were calculated for inter-rater reliability based on a two-way, random effects model with mean-rating (k = 2) and absolute agreement; a two-way mixed-effect model, based on single rating and absolute agreement, was used to assess the intra-rater reliability for each rater. ICCs were calculated using SPSS statistical package, version 18.0 (SPSS, Chicago, IL). ICC was found acceptable by statistical standards (ICC = 0.8) 79 . Dose-response calibration curve from manual scoring was employed for dose estimation. For the automated scoring using the FociRad, preprocessed images were analyzed by the optimized D1-weighted model for MNC detection, followed by the D2-weighted model for γ-H2AX foci detection and foci counting. Dose-response calibration curve established from FociRad was then used for dose calculation (Fig. 5).
Radiation dose estimation in the unseen dataset was performed using the in-house dose-response calibration curves (i.e. the relationship between radiation dose against mean number of γ-H2AX foci per cell). To establish a dose-response calibration curve, mean numbers of foci per cell were scored manually by a trained biologist and automatically by FociRad. The FociRad was employed to establish dose-response calibration curves using the D1-W for MNC detection, and then the D2-W for γ-H2AX foci detection. Mean numbers of γ-H2AX foci per cell from manual scoring and the FociRad were fitted individually using a linear function. SPSS statistical package, version 18.0 (SPSS, Chicago, IL) was used for curve fitting. The established dose-response calibration curves for both manual and automated scoring were linear and fitted with the equation y = ax + b where y was the mean number of γ-H2AX foci per cell and x was the absorbed dose (Gy). Dose-response calibration curves established from both manual and automated FociRad scorings were employed for converting foci counts into dose estimates.
Estimated doses obtained from both methods were used to calculate mean absolute difference (MAD) values 80 as in Eq. (4).

Results
Model performance metric results. The performance evaluation metrics included precision, recall, and F1 score. These metrics of the γ-H2AX foci detection (FociRad) model for irradiated doses of 0, 1, 2, 4 and 6 Gy were presented in Table 1. Overall model performance metrics were 0.924, 0.897, and 0.911 for precision, recall and F1 score, respectively.

Agreement of γ-H2AX foci assessments between manual and automated (FociRad) scor-
ing. γ-H2AX foci per cell determined by manual scoring compared with FociRad scoring were assessed with intra-class correlation coefficients (ICCs) 78 using SPSS version 18.0 (SPSS, Chicago, IL). Bland-Altman analysis 81 was used to visually determine systemic differences, and estimate their means and limits of agreement (LOA), between manual and automated scorings. Bland-Altman plots, which presented the differences between the two methods versus the mean values from manual scoring with the representation of the LOA (from -1.96 SD to + 1.96 SD), were constructed and evaluated. For determining the extent of agreement between the two methods, ICC estimates 78 and their 95% confidence intervals (CIs) were calculated based on absolute-agreement and two-way random effects models. Statistical analyses were performed and plots created with SPSS version 18.0. Mean numbers of γ-H2AX foci obtained from the FociRad showed very good agreement [ICC = 0.970 (0.962-0.977)] with those from manual scoring (see Supplementary Fig. S2A online); mean foci difference (mean number of foci Human − foci FociRad ) was -0.001 ± 0.874 foci (see Supplementary Fig. S2B online).

Discussion
The intensity-based approach to quantitate γ-H2AX for dose estimation is a more rapid and reliable method for detecting DNA damage within the first few hours of radiation exposure compared to other cytogenetic methods which require several days. Still, manual scoring of γ-H2AX foci needs an extensive time for scoring and is labor intensive. Therefore, we proposed an automated foci scoring method (FociRad) using a concatenated deeplearning model to overcome these limitations and compared performance with manual scoring. Several groups 32,59,60 successfully developed deep learning methods in automated foci scoring in different types of cells and yielded very good performances as mentioned in the introduction section. Moreover, Vicar et al. 68 studied using 3D data and in heterogeneous cell populations. Previous studies 32,59,60,68 neither used YOLO algorithm for object detection nor studied in human blood samples. FociRad, the two-stage learning method of the YOLO v4 for MNC and γ-H2AX foci detection, was applied in whole blood and yielded a very good F1 score of 0.911 (min 0.762, max 0.933). Moreover, automated scoring had very good correlation with manual scoring at 0, 1, 2 Gy (ICC = 0.90-0.94), and good correlation at 4 Gy (ICC = 0.80) and 6 Gy (ICC = 0.86). At higher doses, the dispersion of data between automated and manual scoring may have been due to the difficulties in differentiating overlapped foci. This is observed at high doses 36,50 and limits the γ-H2AX assay using fluorescence microscopy. To address this limitation, the adaptive FociRad model using deep learning-based clustering approaches 82 should be studied in three-dimensional confocal images in the future.
In radiation emergency, biodosimetry network played an important role in radiation dose estimation. A previous study by Rothkamm et al. 80 showed that the MAD values between estimated and actual doses based on the scoring of foci by four participating laboratories could be categorized into two groups: low MAD (0.5 to 0.7 Gy) and high MAD (1.3 to 1.7 Gy). In this study, FociRad model was implemented to estimate the radiation dose from an unseen dataset compared to manual scoring using the established dose-response calibration curves.
The MAD values of 0, 2, 4 and 6 Gy actual dose were within the differences of ± 1 Gy for both scoring methods. Our average MAD values were within their low MAD group from the estimation using both manual (0.56 Gy) and FociRad (0.62 Gy) scoring.
FociRad is a fast-processing approach compared with manual scoring. The evaluation times (per 100 cropped images) used by two observers for manual scoring and used by FociRad for automated scoring of the unseen dataset were assessed (see Supplementary Table S2 online). FociRad used consistently shorter times (less than 0.5 min), while manual scoring used longer times which varied with observer. In addition, manual scoring of γ-H2AX foci tended to consume more evaluation time at higher doses as seen from average evaluation times of sevenfold, tenfold and 14-fold at 2, 4 and 6 Gy compared to that at 0 Gy, respectively. This was likely due to the number of overlapped foci in high dose images. Fast evaluation time is important for the application of FociRad in large scale accidents, as potential biodosimetry tools must rapidly estimate dose prior to further medical care. Increasing need of well-established biodosimetry laboratories is important for radiation emergency preparedness. An example is the inter-laboratory comparison exercises conducted by established biodosimetry laboratories as in the European Network of Biodosimetry (RENEB) 83,84 . However, some discrepancies arise between dose estimations from different laboratories 80,83,84 possibly due to variation in staining and imaging set ups as well as observer-dependent scoring. Using a single foci counting-based, deep learning platform such as FociRad may reduce these inconsistencies.
This study has some limitations, and so further studies will be required. First, the calibration curves were established based on a single timepoint (1 h post-irradiation). However, this time point was within the window of peak γ-H2AX responses (between 30 min and 2 h) 11,85 . Following the induction of γ-H2AX, both a fast and slow decay of the γ-H2AX signal is observed within 24 h and up to a few days 24,86 . It is suggested that calibration curves be established at various time points for wider application. Second, the cell surface marker used in this study (i.e. CD45) is a leukocyte common marker which is not specific to a WBC subpopulation. For our analysis we selected MNCs (lymphocytes and monocytes) rather than polymorphonuclear cells (granulocytes) due to the higher expression of radiation-induced γ-H2AX in these cells 85,87 . Nonetheless, using CD45 as a surface marker may have helped FociRad increase the accuracy of cell recognition in the first-stage model. Moreover, FociRad might also be helpful in quantification of DNA damage resulting from anti-CD45 targeted radioimmunotherapy [88][89][90] . To further develop the FociRad model for γ-H2AX assessment in different blood cell populations, one could modify the model by staining with other specific surface markers (e.g. T cell -CD3, B cell -CD19, NK cell -CD56, monocyte -CD14, granulocyte -CD15). Finally, FociRad should be further implemented with available external datasets from various sources of radiation (e.g. gamma rays, alpha particles and neutrons), and different energy ranges to reflect those used in diagnostic radiology, nuclear medicine and radiotherapy.

Conclusion
An automated γ-H2AX foci scoring model (FociRad), with a novel two-stage deep-learning approach based on a YOLO algorithm, was proposed to overcome the limitations of manual scoring of γ-H2AX foci in peripheral blood samples. FociRad functioned with very high performance (F1 > 0.9) and rapid evaluation time (less than 0.5 min per 100 images), when compared to manual scoring which is tedious, observer-dependent and timeconsuming. FociRad may be used for rapid and large-scale analysis of γ-H2AX foci, such as is needed for medical triage after a radiation incident.

Data availability
The data that support the findings of this study are available at https:// github. com/ Khais ang/ FociR ad. git. Furthermore, CiRA CORE can be requested and downloaded on https:// git. cira-lab. com/ cira.